Texture Mapping for Voxel Models Using SOM

Abstract

In this article, we propose an innovative algorithm for texture-mapping voxel-based models. Voxel-based models are composed of voxels. Their surfaces are digitalized and basic geometrical information, like normal and tangent vectors, are absent from their representations. Relying on connectivity and geometrical information to parametrize the surface of a voxel-based model is impossible. Instead, we derive an automatic mapping procedure, based on Self-Organizing Map (SOM), to parametrize its surface voxels. First, we use an unsupervised training to convert the SOM lattice into an approximation surface of the model by using the surface voxels as input data. Then, another unsupervised training is triggered to parameterize the nodes of the SOM lattice by using the texels of the texture as input data. In the $3^{rd}$ stage, the surface voxels are textured, based on the relations established in the two training processes. As a result, the mapping task is efficiently accomplished without too much human interference.